NLP based Machine Learning Approaches for Text Summarization
Rahul, Surabhi Adhikari, Monika
Abstract
Due to the plethora of data available today, text summarization has become very essential to gain just the right amount of information from huge texts. We see long articles in news websites, blogs, customers' review websites, and so on. This review paper presents various approaches to generate summary of huge texts. Various papers have been studied for different methods that have been used so far for text summarization. Mostly, the methods described in this paper produce Abstractive (ABS) or Extractive (EXT) summaries of text documents. Query-based summarization techniques are also discussed. The paper mostly discusses about the structured based and semantic based approaches for summarization of the text documents. Various datasets were used to test the summaries produced by these models, such as the CNN corpus, DUC2000, single and multiple text documents etc. We have studied these methods and also the tendencies, achievements, past work and future scope of them in text summarization as well as other fields.